What the data for this category looks like
Commercial property marketing content comes from four main sources: leasing management systems, offline promotional materials, online promotion copy, and tenant communication records. Content update cycles vary. Only small amounts of occupancy information are updated daily. Update frequency rises during leasing adjustments or commercial district event preparation.
Document structure includes two content types. Structured field documents, such as shop listing Excel files, contain clear fields like shop number, rental area, and unit rent. Unstructured text documents, such as PDF leasing manuals and event posts, include promotional copy, site descriptions, and format requirements. Some documents use standardized units: area is measured in square meters, and rent is measured in yuan per square meter per day.
What constraints do these characteristics impose on vector models and indexing?
First, mixed structured and unstructured content requires vector models to support vectorization of both text and numeric fields. This prevents loss of semantic associations from structured information. Second, inconsistent update cycles require indexing systems to support incremental updates. This avoids resource waste caused by full index rebuilds. Third, fields with standardized units require retaining unit information during vectorization. This prevents confusion between similar data with different units. Fourth, multi-format source documents require parsing modules compatible with common formats including Excel, PDF, and Word. The modules must accurately extract both structured fields and unstructured text.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
chunk_size | 800–1200 characters | Commercial property marketing content includes long sections of leasing policies and short-title rental listings. This range balances context completeness and vector recall accuracy |
chunk_overlap | 100–150 characters | Structured fields like shop area and rent must retain contextual connections after splitting, to avoid breaking critical information |
recall_top_k | Top 8–12 results | Customer acquisition scenarios need to cover shop options across multiple formats and areas. Too many recall results increase inference load, while too few fail to meet matching needs |
similarity_threshold | 0.72–0.80 | Filters low-match non-target shop information, while retaining compatible matching results for different formats |
index_incremental_update | Enabled | Commercial property marketing content has inconsistent update cycles. Incremental updates reduce hardware resource consumption from full index rebuilds |
parse_excel_sheet_names | Specify tenant listings, leasing announcement worksheets | Excel files for commercial property marketing data often contain multiple irrelevant worksheets. Only parsing specified sheets reduces invalid index content |
Three Common Mistakes
- Phenomenon: Exported index data only contains the overall knowledge base content, and cannot be exported split by shop format or rent range. Cause: The
index_export_filterparameter is not configured, and export rules are not set according to business categories. - Phenomenon: Vector models deployed on ARM soft routers fail to load normally, with errors indicating incompatible instruction sets. Cause: A vector model image only supporting x86 architecture is selected, and no adaptation for ARM instruction sets is made.
- Phenomenon: Calls to external vector model APIs return 503 errors, and index construction cannot be completed. Cause: The
vector_model_api_timeoutparameter is not configured, and the response timeout threshold is set too short, without adapting to the inference delay of non-GPU models.
How to confirm proper configuration
- Upload a typical commercial property marketing document, check the parsed segmented results, and confirm that segment lengths match the configured
chunk_sizeandchunk_overlapparameters. - Initiate a vector recall test, input a query containing target shop characteristics, and verify whether the number of recall results and similarity scores match the preset configuration.
- Trigger an incremental index update, check the index update log, and confirm that only newly added or modified content is included in the index, with no full rebuild triggered.
- Export a portion of index data, and verify that the exported content complies with preset business category filtering rules.
The parameter values provided on this page are common recommended starting points for configuration. Actual values are affected by material form, data volume, and business rules. Specific issues require case-by-case analysis. It is recommended to test on your own samples before finalizing settings.
Question material comes from public community discussions. Configuration values are common starting points and should be measured against your own samples. Verified on 2026-09-14.